Machine learning system for workload failover in a converged infrastructure

Abstract

Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Therefore, the following is claimed: 
     
       1. A method, comprising:
 identifying, by at least one computing device, a cluster of virtual machines executed in computing environment; 
 performing, by the at least one computing device, a plurality of simulations for the cluster of virtual machines, the plurality of simulations simulating a failure of one or more hosts in the computing environment, the plurality of simulations further simulating an effect on the cluster of virtual machines as a result of the failure; 
 generating, by the at least one computing device, a score for respective ones of the simulations, the score representing the effect on the cluster of virtual machines; 
 performing, by the at least one computing device, a clustering process on one of the simulations based upon on the score, the clustering process being trained using data from at least one other deployment within a converged infrastructure environment; and 
 identifying, by the at least one computing device, based on the clustering process, a most similar deployment to the cluster of virtual machines within the computing environment. 
 
     
     
       2. The method of  claim 1 , wherein the plurality of buckets comprise one of: a first prediction that the cluster of virtual machines will restart with minimal resource degradation, a second prediction that the cluster of virtual machines will restart with resource degradation, or a third prediction that the one or more of the cluster of virtual machines will not restart. 
     
     
       3. The method of  claim 1 , wherein the plurality of policy parameters specify at least one of: a CPU reservation policy for the plurality of hosts, a memory reservation policy for the plurality of host, a failover level policy, or a host-specific failover policy. 
     
     
       4. The method of  claim 1 , wherein the clustering process comprises a k-means clustering process performed on the score and the policy parameters. 
     
     
       5. The method of  claim 1 , wherein identifying a most similar deployment further comprises:
 identifying a configuration of another deployment of a cluster of virtual machines having the smallest Euclidian distance between a first point representing the one of the simulations and a second point representing the other deployment, wherein the other deployment is associated with a prediction that a corresponding cluster of virtual machines associated with the other deployment will restart with minimal resource degradation. 
 
     
     
       6. The method of  claim 5 , further comprising:
 generating at least one recommendation to modify the plurality of policy parameters to match a corresponding plurality of policy parameters associated with the other deployment. 
 
     
     
       7. The method of  claim 1 , wherein the score represents an availability and performance score. 
     
     
       8. A system comprising:
 at least one computing device; 
 an application executed by the at least one computing device, the application causing the at least one computing device to at least:
 identify a cluster of virtual machines executed in computing environment; 
 perform a plurality of simulations for the cluster of virtual machines, the plurality of simulations simulating a failure of one or more hosts in the computing environment, the plurality of simulations further simulating an effect on the cluster of virtual machines as a result of the failure; 
 generate a score for respective ones of the simulations, the score representing the effect on the cluster of virtual machines; 
 perform a clustering process on one of the simulations based upon on the score, the clustering process being trained using data from at least one other deployment within a converged infrastructure environment; and 
 identify based on the clustering process, a most similar deployment to the cluster of virtual machines within the computing environment. 
 
 
     
     
       9. The system of  claim 8 , wherein the plurality of buckets comprise one of: a first prediction that the cluster of virtual machines will restart with minimal resource degradation, a second prediction that the cluster of virtual machines will restart with resource degradation, or a third prediction that the one or more of the cluster of virtual machines will not restart. 
     
     
       10. The system of  claim 8 , wherein the plurality of policy parameters specify at least one of: a CPU reservation policy for the plurality of hosts, a memory reservation policy for the plurality of hosts, a failover level policy, or a host-specific failover policy. 
     
     
       11. The system of  claim 8 , wherein the clustering process comprises a k-means clustering process performed on the score and the policy parameters. 
     
     
       12. The system of  claim 8 , wherein a most similar deployment is identified by:
 identifying a configuration of another deployment of a cluster of virtual machines having the smallest Euclidian distance between a first point representing the one of the simulations and a second point representing the other deployment, wherein the other deployment is associated with a prediction that a corresponding cluster of virtual machines associated with the other deployment will restart with minimal resource degradation. 
 
     
     
       13. The system of  claim 12 , wherein the application further causes the at least one computing device to at least:
 generate at least one recommendation to modify the plurality of policy parameters to match a corresponding plurality of policy parameters associated with the other deployment. 
 
     
     
       14. The system of  claim 8 , wherein the score represents an availability and performance score. 
     
     
       15. A non-transitory computer-readable medium embodying a program executed by at least one computing device, the program causing the at least one computing device to at least:
 identify a cluster of virtual machines executed in computing environment; 
 perform a plurality of simulations for the cluster of virtual machines, the plurality of simulations simulating a failure of one or more hosts in the computing environment, the plurality of simulations further simulating an effect on the cluster of virtual machines as a result of the failure; 
 generate a score for respective ones of the simulations, the score representing the effect on the cluster of virtual machines; 
 perform a clustering process on one of the simulations based upon on the score, the clustering process being trained using data from at least one other deployment within a converged infrastructure environment; and 
 identify based on the clustering process, a most similar deployment to the cluster of virtual machines within the computing environment. 
 
     
     
       16. The non-transitory computer-readable medium of  claim 15 , wherein the plurality of buckets comprise one of: a first prediction that the cluster of virtual machines will restart with minimal resource degradation, a second prediction that the cluster of virtual machines will restart with resource degradation, or a third prediction that the one or more of the cluster of virtual machines will not restart. 
     
     
       17. The non-transitory computer-readable medium of  claim 15 , wherein the plurality of policy parameters specify at least one of: a CPU reservation policy for the plurality of hosts, a memory reservation policy for the plurality of hosts, a failover level policy, or a host-specific failover policy. 
     
     
       18. The non-transitory computer-readable medium of  claim 15 , wherein the clustering process comprises a k-means clustering process performed on the score and the policy parameters. 
     
     
       19. The non-transitory computer-readable medium of  claim 15 , wherein a most similar deployment is identified by:
 identifying a configuration of another deployment of a cluster of virtual machines having the smallest Euclidian distance between a first point representing the one of the simulations and a second point representing the other deployment, wherein the other deployment is associated with a prediction that a corresponding cluster of virtual machines associated with the other deployment will restart with minimal resource degradation. 
 
     
     
       20. The non-transitory computer-readable medium of  claim 19 , wherein the application further causes the at least one computing device to at least:
 generate at least one recommendation to modify the plurality of policy parameters to match a corresponding plurality of policy parameters associated with the other deployment.